Abstract :
[en] We present a cooperative framework for content-based image retrieval
for the realistic setting where images are distributed across
multiple cooperating servers. The proposed
method is in line with bag-of-features approaches but uses fully
data-independent, randomized structures, shared by the cooperating
servers, to map image features to common visual words. A
coherent, global image similarity measure (which is a kernel) is computed in a distributed fashion over visual
words, by only requiring a small amount of data transfers between
nodes. Our experiments on various image types show that this framework
is a very promising step towards large-scale, distributed content-based image retrieval.
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